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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Kersting, Lukas
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Publications (6/6 displayed)
- 2024Improving the flow forming process by a novel closed-loop control
- 2023Softsensors: key component of property control in forming technologycitations
- 2023Control strategy for angular gradations by means of the flow forming process
- 2022Coupled microscopic and micromagnetic depth-specific analysis of plastic deformation and phase transformation of metastable austenitic steel AISI 304L by flow formingcitations
- 2022Innovative Online Measurement and Modelling Approach for Property-Controlled Flow Forming Processescitations
- 2022A flow forming process model to predict workpiece properties in AISI 304Lcitations
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article
A flow forming process model to predict workpiece properties in AISI 304L
Abstract
<jats:p>The control of workpiece properties enables an application-oriented and time-efficient production of components. In reverse flow forming, e.g., the control of the microstructure profile, in contrast to the adjustment of the geometry, is not yet part of the state of the art. This is particularly challenging when forming seamless tubes made of metastable austenitic stainless AISI 304L steel. In this steel, a phase transformation from austenite to martensite can occur due to mechanically and/or thermally induced energy. The α’-martensite has different mechanical and micromagnetic properties, which can be advantageous depending on the application. For the purpose of local property control, the resulting α’-martensite content should be measured and controlled online during the forming process. In this paper, results from an empirical correlation model of process parameter combinations and resulting α’-martensite content as well as geometry will be presented. Based on this, the focus of the paper will be on process modeling by means of FEM in order to create the transition to a numerically supported process model. Furthermore, it will be specified how the numerical process model can be used in a predictive manner for an online closed-loop process control.</jats:p>